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Abyss Processing - Exploring the Deep in Medical Images
by Devalla Sripad Krishna; Jean Martial Mari; Dieter Trau; Michaël J. A. Girard

Medical imaging is a non-invasive method for clinicians to understand what is happening deep inside our body. The most common medical imaging techniques include MRI (Magnetic Resonance Imaging), CT (Computed Tomography), X-Ray, Ultrasound (US), Optical Coherence Tomography (OCT), etc. One of the biggest advantages of OCT [1] and US [2] over the other techniques includes its cost effectiveness, reduced risk of exposure to radiation and its ability to study soft tissues.

However, the image quality and interpretation of US and OCT images largely depend on various factors such as the skill of the person scanning, method of scanning, etc. A reduced image quality makes it hard for clinical diagnosis and reduces its clinical relevance. Over the recent years, the quality of OCT and US images have been significantly improved to visualize deep tissue structures without the need of surgical interventions. However, the quality of these images [3] is still largely hampered by the presence of artifacts and by poor tissue visibility in deep layers. This is due to signal attenuation [4-5], whereby signal strength diminishes as a function of tissue depth [1] and presence of strongly attenuating and scattering structures such as blood vessels. These form a barrier to clinical relevance of OCT and US images and prevent the diagnosis and risk management of multiple pathologies.

To overcome the above limitations due to signal attenuation and blood vessel shadows, Abyss Processing Pte. Ltd., a new Singapore based start-up aims to provide software solutions to significantly increase the quality of medical images in order to have a direct impact on clinical diagnosis and treatment. Abyss Processing is a spin off from The Ophthalmic Engineering and Innovation Lab, led by Dr. Michael Girard, Department of Biomedical Engineering, National University of Singapore. It is also co-founded by Dr. Jean Martial Mari, University of French Polynesia and Dr. Dieter Trau, National University of Singapore. It offers solutions to increase quality of images in areas such as ophthalmology, cardiology and dermatology through its software Reflectivity [6] (Patent Pending). Reflectivity was successfully beta-tested by over 100 users from 27 top medical institutions across 4 continents, resulting in over 30 referred publications in top medical journals.

Reflectivity is a user friendly, stand alone and cross platform supported software encompassing novel compensation [7-10] and digital staining technology that can drastically improve the quality of existing OCT and US images in a post-processing step. Reflectivity has currently been validated for its applications in Ophthalmic [7-10] and Cardiovascular applications [11-12]. Its applications in Ophthalmology include improving visibility of anterior lamina cribrosa (LC) [8], lamina/sclera insertions [7-8], LC defects, drusen regions and also to identify the thickness of the choroidal region [13]. In optic nerve head (ONH) tissue, it removes artifacts, increases contrast and significantly increases the visibility of the deepest tissue layers without introducing noise artifacts. This is possible by the use of standard compensation [7] and adaptive compensation [8] techniques. From a clinical perspective, the visibility enhancement of the above discussed tissues of ONH are very important, especially the LC. Improving the visibility of the tissues not only helps to study the biological properties but also understanding the biomechanical properties [14-17]. Studying these biological and biomechanical properties of the LC are of prime importance in the pathophysiology of glaucoma, the second leading cause for blindness [18]. The visibility of cornea has also been significantly increased through corneal adaptive compensation algorithm [10]. The results for the adaptive compensation of optic nerve and corneal adaptive compensation are shown in Figure 1. Besides enhancing and improving the visibility of tissues, digital staining can also be performed using the latest version of Reflectivity. Digital staining helps to highlight connective and neural tissues which are of specific interest to the clinicians. The results of digital staining are shown in Figure 2.

The cardiovascular applications of this software include enhancing plaque [11-12] and blood vessel layer contrast, improving the visibility of the deep tissues in arteries, removal of shadow artifacts from these dense structures and also to allow better identification of external plaque boundaries. The results of this application is shown in Figure 3. These applications have a clinical advantage of improving the biological study of plaques. This is critical as undetected build-up of plaque can lead to atherosclerosis [19], which in turn can lead to heart disease, heart attack, and stroke.

Besides improving and enhancing soft tissue visibility to study biological properties for better clinical diagnosis, we are also able to study biomechanical response and in turn their properties as well. This has also led to development of 3D strain mapping techniques to study the optic nerve [20-22]. This strain mapping helps to extract intraocular pressure induced optic nerve head 3D deformation and biomechanical strain using in vivo imaging [21-22]. The 3D deformation map helps us to understand the correlation between material properties of the tissues and glaucoma.

The compensated and enhanced images were also used in the development of global shape index to characterize anterior LC morphology which could be implicated in the progression of glaucoma [23].

The key features of this software include its simple graphical interface, interaction with license server (a strong anti-piracy system which allows the user to use the software only on registered computers), cross platform functionality making it compatible for Mac OS, Linux and Windows based systems and its ability to read multiple input image formats (both 2D and 3D files). Figure 4 shows the screen capture of the software. Another key feature of Reflectivity is its ability to enhance images across different OCT devices and provide superior results as compared to existing hardware technique of enhanced depth imaging (EDI) [24]. Also, the latest version is compatible with all current OCT and US devices in the market, thus making it a very generic post-processing tool for clinicians and researchers to work with irrespective of the hardware they use for imaging.

With a current market size of about 20,000 OCT machines and about 110,000 US machines in the market, the primary application of Reflectivity remains post processing for medical imaging catering to the needs of clinicians, researchers in academia and private institutes, imaging technicians, healthcare institutions, large companies and start-ups involved in image processing R&D. 
The potential secondary market includes radar imaging with military application or SONAR imaging (submarine, ship/airplane wreck detection) and applications in astronomy (comet/asteroid imaging) as well.

Reflectivity can also be licensed as OEM solution to existing imaging hardware companies to be incorporated to an imaging hardware device under a license agreement.

Reflectivity is available for free as a limited time offer to academic institutions and at cost for commercial users. For further details on the software and other commercial enquiries please contact at [email protected] .


  1. Fujimoto, J. G., Pitris, C., Boppart, S. a., & Brezinski, M. E. (2000). Optical Coherence Tomography: An Emerging Technology for Biomedical Imaging and Optical Biopsy. Neoplasia, 2(1–2), 9–25. https://doi.org/10.1038/sj.neo.7900071 .

  2. Ziskin, M. C. (1987). Applications of Ultrasound in Medicine --- Comparison with Other Modalities. In M. H. Repacholi, M. Grandolfo, & A. Rindi (Eds.), Ultrasound:Medical Applications, Biological Effects, and Hazard Potential (pp. 49–59). inbook, Boston, MA: Springer US. https://doi.org/10.1007/978-1-4613-1811-84 .

  3. J. M. Schmitt, Optical coherence tomography (OCT): a review, in IEEE Journal of Selected Topics in Quantum Electronics, vol. 5, no. 4, pp. 1205-1215, Jul/Aug 1999.doi: 10.1109/2944.796348.

  4. Van de Hulst HC. Light Scattering by Small Particles. Mineola, NY: Dover Publications; 1981.

  5. Chang S, Flueraru C, Mao, et al. Attenuation compensation for optical coherence tomography imaging. Optics Communications. 2009;282:4503–4507.

  6. https://www.bioeng.nus.edu.sg/OEIL/reflectivity.shtml accessed on 14th Nov,2016.

  7. Girard MJA, Strouthidis NG, Ethier CR, et al. Shadow Removal and Contrast Enhancement in Optical Coherence Tomography Images of the Human Optic Nerve Head. Invest Opthalmol and Vis Sci. 2011; 52(10):7738-48.

  8. Mari JM, Park SC, Strouthids NG, et al. Enhancement of Lamina Cribrosa Visibility in Optical Coherence Tomography Images Using Adaptive Compensation. Invest Ophthalm and Vis Sci. 2013; 54(3):2238-47.

  9. Kim TW, Wollstein G, Leung C, et al. Imaging of lamina cribrosa in glaucoma: Perspectives of pathogenesis and clinical applications. Current Eye Research. 2013; 38(9):903-9.

  10. Girard MJA, Ang M, Chung CW, et al. Enhancement of Corneal Visibility in Optical Coherence Tomography Images using Corneal Adaptive Compensation. 2015. Translational Vision Science & Technology. 2015 May 15;4(3):3. doi: 10.1167/tvst.4.3.3.

  11. Foin N, Mari JM, Davies JE, et al. Imaging of Coronary Artery Plaques using Contrast-enhanced Optical Coherence Tomography. European Heart Journal – Cardiovascular Imaging. 2013; 14(1):85.

  12. Foin N, Mari JM, Di Mario C, et al. Intracoronary Imaging using Attenuation-compensated Optical Coherence Tomography Allows Better Visualisation of Coronary Artery Diseases. Cardiovascular Revascularization Medicine. 2013; 14(3):139-143.

  13. Gupta P, Sidhartha E, Girard MJA, et al. A Simplified Method to Measure Choroidal Thickness Using Adaptive Compensation in Enhanced Depth Imaging Optical Coherence Tomography. PLOS ONE. 2014; 9(5):e96661.

  14. Girard MJA, Strouthidis NG, Desjardins A, et al. In Vivo Optic nerve Head Biomechanics: Performance Testing of a 3D tracking algorithm. Journal of the Royal Society Interface. 2013; 10(87):20130459.

  15. Girard MJA, Dupps WJ, Mani B, et al. Translating Ocular Biomechanics into Clinical Practice: Current State and Future Prospects. Current Eye Research. 2014; 15:1-18.

  16. Zhang L, Albon J, Jones H, et al. Collagen Microstructural Factors Influencing Optic Nerve Head Biomechanics. Invest Ophthalmol Vis Sci. 2015 Mar 3;56(3):2031-42. doi: 10.1167/iovs.14-15734.

  17. Zhang L, Thakku SG, Beotra MR, et al. Verification of A Virtual Fields Method to Extract the Mechanical Properties of Human Optic Nerve Head Tissues In Vivo. 2016. Submitted to Biomechanics and Modeling in Mechanobiology.

  18. Resnikoff S, Pascolini D, Etya’ale D, et al. Global data on visual impairment in the year 2002. Bull World Health Organ. 2004;82: 844 – 851.

  19. Crowther, M. A. (2005). Pathogenesis of Atherosclerosis. ASH Education Program Book, 2005(1), 436–441. JOUR. https://doi.org/10.1182/asheducation-2005.1.436

  20. Girard MJA, Beotra MR, Chin KS, et al. In vivo 3D Strain Mapping of the Optic Nerve Head Following IOP Lowering by Trabeculectomy. 2016. Ophthalmology. 2016 Mar 16. pii: S0161-6420(16)00170-6.

  21. Wang X, Beotra MR, Tun TA, et al. In Vivo 3-Dimensional Strain Mapping Confirms Large Optic Nerve Head Deformations Following Horizontal Eye Movements. 2016. Invest Ophthalmol Vis Sci. October 2016, Vol.57, 5825-5833. doi:10.1167/iovs.16-20560.

  22. Zhang L, Thakku SG, Beotra MR, et al. Verification of A Virtual Fields Method to Extract the Mechanical Properties of Human Optic Nerve Head Tissues In Vivo. 2016. Submitted to Biomechanics and Modeling in Mechanobiology.

  23. Thakku SG, Tham Y-C, Bas- karan M, et al. A global shape index to characterize anterior lamina cribrosa morphology and its determinants in healthy Indian eyes. Invest Ophthal- mol Vis Sci. 2015;56:3604–3614. DOI:10.1167/iovs.15-16707.

  24. Girard MJA, Tun TA, Husain R, et al. Lamina Cribrosa Visibility using Optical Coherence Tomography: Comparison of Devices and Effects of Image Enhancement Techniques.
IOVS. 2015 15;56(2):865-74.

About the Authors

Devalla Sripad Krishna is currently a PhD student in the Department of Biomedical Engineering at the National University of Singapore. He received his B.Tech. in Electrical and Electronics Engineering from National Institute of Technology Trichy, India(2012-2016). He had been working in the field of robotics, extensively in domains such as control systems, hardware design and image processing. He was the vice president of the Robotics and Machine Intelligence group at National Institute of Technology Trichy, India. He has developed image processing algorithms for path planning of rovers and for other robotics applications. He is currently working on developing novel compensation and staining techniques to improve the quality of OCT images.

Dr Jean Martial Mari, founder, is one of the main inventor of the compensation technology. He is an Associate Professor (tenured) in Computer Science in the Department of Health, Science and Technology at the University of French Polynesia working on signal and image processing. In 2004 he received his PhD in Acoustics and in 2012 his French "Habilitation to Lead Research" (HDR) from the Université Claude Bernard Lyon 1 (France). Dr Mari has been successively Research Assistant at the Medical Vision Laboratory, Engineering Science Department (University of Oxford), Research Associate at the Bioengineering Department (Imperial College London), Researcher at the INSERM 1032 (France), and Research Associate in the Photoacoustic Imaging Group of the Biomedical Optics Research Laboratory (University College London). Dr Mari expertise lies in theoretical and experimental signal and image processing. He is the co-inventor of two patents in ultrasound and flow imaging.

Dr Michaël Girard, founder, is one of the main inventor of the compensation technology. He is an Assistant Professor in the Department of Biomedical Engineering at the National University of Singapore (NUS) and the Co-Head of the Bioengineering & Devices Research Group at the Singapore Eye Research Institute (SERI). He was awarded his PhD from the Department of Biomedical Engineering at Tulane University, New Orleans, USA, in January of 2009. Subsequently, Dr Girard pursued his postdoctoral work in the Department of Bioengineering at Imperial College London, UK. In September of 2010, Dr Girard was awarded a highly competitive and prestigious Imperial College Junior Research Fellowship to pursue his research. Throughout his training, Dr Girard has gained expertise in experimental, theoretical, and computational biomechanics/bioimaging, which he has utilized to pose and answer questions of high clinical relevance in the field of ophthalmology.

Dr Dieter Trau, founder, started his entrepreneurial activities as a founder of a life-science company in 1995. In 2010 Dieter founded the AyoxxA Group in Singapore and Germany, now a 25 million EURO VC funded company employing 30 people. Recently he started Tip Biosystems Pte Ltd in Singapore, the company is launching its personal photometer products in 2017. Dieter holds a PhD in Chemistry from the Hong Kong University of Science and Technology (HKUST). He was a Visiting Assistant Professor at HKUST and since 2004 has been an Assistant and now Associate Professor at the Department of Biomedical Engineering and the Department of Chemical & Biomolecular Engineering at the National University of Singapore (NUS). He is the author of >40 international peer-reviewed research papers and inventor of 16 patent families, resulting in more than 80 patent applications of which 20 are granted and commercialized various companies.

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